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A novel differential evolution algorithm for no-idle permutation flow-shop scheduling problems

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  • Quan-Ke Pan
  • Ling Wang

Abstract

A novel Discrete Differential Evolution (DDE) algorithm is proposed in this paper for solving no-idle permutation flow-shop scheduling problems with maximum completion time (makespan) criterion. Firstly, individuals of the DDE algorithm are represented as discrete job permutations, and new mutation and crossover operators are developed. Secondly, a local search algorithm based on insert neighbourhood is embedded in the DDE algorithm to balance the exploration and exploitation and to enhance the local searching ability. In addition, we present two simple approaches to calculate makespan and a speed-up method for insert neighbourhood to improve the efficiency of the whole algorithm. Computational simulations and comparisons based on some well-known benchmarks demonstrate that the DDE algorithm is not only superior to the improved greedy and Kalczynski-Kamburowski heuristics in terms of searching quality, but also superior to the particle swarm optimisation and differential evolution algorithms according to searching quality, robustness and efficiency. [Received 9 July 2007; Revised 10 October 2007; Accepted 30 October 2007]

Suggested Citation

  • Quan-Ke Pan & Ling Wang, 2008. "A novel differential evolution algorithm for no-idle permutation flow-shop scheduling problems," European Journal of Industrial Engineering, Inderscience Enterprises Ltd, vol. 2(3), pages 279-297.
    • Handle: RePEc:ids:eujine:v:2:y:2008:i:3:p:279-297
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    Citations

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    Cited by:

    1. Zewen Sun & Xingsheng Gu, 2017. "Hybrid Algorithm Based on an Estimation of Distribution Algorithm and Cuckoo Search for the No Idle Permutation Flow Shop Scheduling Problem with the Total Tardiness Criterion Minimization," Sustainability, MDPI, vol. 9(6), pages 1-16, June.
    2. Pan, Quan-Ke & Ruiz, Rubén, 2012. "An estimation of distribution algorithm for lot-streaming flow shop problems with setup times," Omega, Elsevier, vol. 40(2), pages 166-180, April.
    3. Pan, Quan-Ke & Gao, Liang & Li, Xin-Yu & Gao, Kai-Zhou, 2017. "Effective metaheuristics for scheduling a hybrid flowshop with sequence-dependent setup times," Applied Mathematics and Computation, Elsevier, vol. 303(C), pages 89-112.
    4. Tolga Bektaş & Alper Hamzadayı & Rubén Ruiz, 2020. "Benders decomposition for the mixed no-idle permutation flowshop scheduling problem," Journal of Scheduling, Springer, vol. 23(4), pages 513-523, August.
    5. Pan, Quan-Ke & Ruiz, Rubén, 2014. "An effective iterated greedy algorithm for the mixed no-idle permutation flowshop scheduling problem," Omega, Elsevier, vol. 44(C), pages 41-50.
    6. Vallada, Eva & Ruiz, Rubén, 2010. "Genetic algorithms with path relinking for the minimum tardiness permutation flowshop problem," Omega, Elsevier, vol. 38(1-2), pages 57-67, February.
    7. Fernando Luis Rossi & Marcelo Seido Nagano, 2022. "Beam search-based heuristics for the mixed no-idle flowshop with total flowtime criterion," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 44(4), pages 1311-1346, December.

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